Group Task
Q1 : Dominant parties in municipalities with +100k habitants
There were only 48 municipalities with over 100k people. 2 of the cities, Cádiz and Dos Hermanas had a varying population that was over 100k in some periods and under in others. We created a table with the winners and total % of vote, then plotted over time. PP have a stronghold in some areas.
Q2 : Primary Runner-Up Parties to PSOE and PP in Elections
ANY TEXT YOU WANT
<<<<<<< Updated upstream
Q2 : Primary Runner-Up Parties to PSOE and PP in Elections
The original code was a loop that analyzed the second-party votes across all elections, breaking down the results by the size of the cities (in terms of population) where people voted.
Second most voted party by population when the first is PSOE
Q2 : Primary Runner-Up Parties to PSOE and PP in Elections
# Step 1: Create population categories with ordered factors for PP
pp_first <- pp_first |>
mutate(
population_category = factor(
case_when(
population < 10000 ~ "<10.000",
population >= 10000 & population < 50000 ~ ">= 10.000 & < 50.000",
population >= 50000 & population < 100000 ~ ">= 50.000 & < 100.000",
population >= 100000 & population < 500000 ~ ">= 100.000 & < 500.000",
population >= 500000 & population < 1000000 ~ ">= 500.000 & < 1.000.000",
population >= 1000000 ~ ">= 1.000.000"
),
levels = c("<10.000", ">= 10.000 & < 50.000", ">= 50.000 & < 100.000",
">= 100.000 & < 500.000", ">= 500.000 & < 1.000.000", ">= 1.000.000")
)
)
# Step 2: Loop through elections and create a plot for each election for PP
unique_dates_pp <- unique(pp_first$date_elec)
plots <- list()
# Create a list to store plots for PP
plots_pp <- list()
for (date in unique_dates_pp) {
# Ensure `date` is treated as a valid Date object
current_date <- as.Date(date)
# Filter data for the specific election date
data_filtered <- pp_first |>
filter(date_elec == current_date) |>
group_by(population_category, second_party) |>
summarise(
total_votes = sum(second_votes, na.rm = TRUE),
.groups = "drop"
)
# Create the plot
plot <- ggplot(data_filtered, aes(x = population_category,
y = total_votes,
fill = second_party)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
scale_fill_manual(values = party_colors) +
scale_y_continuous(labels = scales::comma) +
labs(
title = paste("Second Party by Population for Election on", format(current_date, "%Y-%m-%d")), # Format the date properly
x = "Inhabitants per city",
y = "Total Votes"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14, hjust = 0.5),
axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
axis.text.y = element_text(size = 10),
legend.position = "bottom",
legend.title = element_blank() # Remove legend title
)
# Save the plot to the list
plots_pp[[as.character(current_date)]] <- plot
}
# Step 3: Display all plots for PP (one at a time)
for (date in unique_dates_pp) {
print(plots_pp[[as.character(as.Date(date))]])
}
Q3 : Impact of low turnout on election outcomes
Low Turnout Areas (<70.7%) PSOE and PP dominate low-turnout areas, securing the highest vote shares. Smaller parties like VOX, CS, and EH-BILDU have moderate presence, while regional parties and new entrants struggle to gain significant traction.
Q3 : Impact of low turnout on election outcomes
Extreme Low Turnout Areas (<45%) EAJ-PNV leads in extreme low-turnout areas, followed closely by PSOE and EH-BILDU. Traditional parties like PP still maintain influence, while smaller parties show minimal presence.
Q4 : Relationship between census and vote (urban vs. rural)
There is no significant overall correlation between population size (log-transformed) and the percentage of votes for parties. The red line indicates a stable trend with no noticeable changes.
Q4 : Relationship between census and vote (urban vs. rural)
Different parties show varying trends in vote percentages across population scales (log-transformed. PP performing better in lower population, while PSOE and PODEMOS-IU perform better in higher population.
Q4 : Relationship between census and vote (urban vs. rural)
Different parties show significant differences in support between rural and urban areas. EH-BILDU and BNG have higher support in rural areas, while MP, CS and PODEMOS-IU have stronger support in urban areas
Q5 : Polling error
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Q5 : Polling error vs days before election
Are polls more precise as we get closer to the election?
Q5 : Polling error vs surveys size
Are surveys conducted on a bigger sample more precise?
Q6 : Visualization
Q7 : Survey projection accuracy over time
Trends in survey prediction biases over time, focusing on bias evolution and accuracy for each party.
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Q8 : Turnout rate over time
How has the turnout rate changed over time? And within each election year, how are turnout rates correlated with the municipalities’ populations?
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We can observe that there is no clear tendency of turnout rate. However, it is interesting seeing that these two ‘snap’ elections in 2016 and 2019 were held shortly after the previous elections, with a very brief gap between each one and its predecessor. And the voter turnout in both of these elections was significantly lower compared to the previous ones, suggesting that the nature of such ‘emergency’ elections may reduce citizens’ willingness to vote again.
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We can observe that there is no clear tendency of turnout rate. However, it is interesting seeing that these two ‘snap’ elections in 2016 and 2019 were held shortly after the previous elections, with a very brief gap between each one and its predecessor. And the voter turnout in both of these elections was significantly lower compared to the previous ones, suggesting that the nature of such ‘emergency’ elections may reduce citizens’ willingness to vote again.
>>>>>>> Stashed changesQ8 : Turnout rate correlated with the municipalities’ populations over time
And apart from the election year and specific election context, how are turnout rates correlated with the municipalities’ populations within each election year?
<<<<<<< Updated upstream
>>>>>>> Stashed changes